Ionospheric slant total electron content mapping algorithm: IONOLAB-SMAP

Global Positioning System (GPS) technology has significantly reshaped navigation and positioning services, providing essential insights across various sectors such as transportation, defense, and scientific research. Total Electron Content (TEC) within the ionosphere, a key parameter for evaluating the influence of ionospheric variations on GPS signal accuracy. These variations, influenced by temporal, geographic, and solar conditions, can cause notable positioning errors, emphasizing the need for precise monitoring and detailed analysis to improve the reliability of GPS-enabled services. TEC values are estimated using dual-frequency GPS receiver measurements that are stored as Receiver INdependent EXchange Format (RINEX) records by using IONOLAB-STEC method. This method, which provides updates every 30 s, incorporates corrections for ionospheric and tropospheric delays, along with timing inaccuracies from satellites and receivers, enhancing the accuracy of GPS positioning. Current Global Ionospheric Maps (GIM) suffer from inadequate temporal and spatial resolution. This research presents an advanced interpolation-based approach for generating high-resolution TEC maps, with a particular emphasis on a preprocessing step applied to the STEC values to reduce the influence of outliers and enhance data quality. This approach allows for the generation of STEC maps with a spatial resolution of 0.48° and a temporal resolution of 5 min, which corresponds to the assumed duration of the ionosphere’s wide-sense stationary period on disturbed days. Moreover, the output maps produced by the proposed method are capable of capturing finer-scale variations in the ionospheric structure, as the STEC data utilized in this study are considered to be closer to raw measurements compared to JPL-TEC and TEC estimates. Our findings highlight the superior performance of the STEC mapping technique over traditional TEC mapping methods, marking a significant advancement in geospatial applications. This methodological innovation promises to enhance GPS reliability and precision across various high-stakes fields, setting a new standard for ionospheric monitoring and supporting advancements in navigation technology and space weather prediction.